Systems and methods for caching augmented reality target data at user devices

ABSTRACT

Systems and methods are disclosed for transmitting, to user devices, data for potential targets predicted to be identified in an augmented reality application. One method includes receiving a request for target data related to at least one physical object within an image of a real-world environment captured at the device; identifying a current target representing the physical object within a virtual environment corresponding to the real-world environment; determining at least one potential future target to be identified at the device based on identified coincident target requests; and sending to the device target data for each of the current and potential future targets based on the determination, wherein the device presents the target data for the current target within the virtual environment displayed at the device and store the target data for the potential future target in a local memory of the device.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to displayingdata on mobile devices in relation to physical locations and objectsand, more particularly, to transmitting and caching data on mobiledevices in relation to physical locations and objects likely to beidentified and requested for display in mobile augmented realityapplications.

BACKGROUND

Augmented reality (AR) applications present users with an augmented orenhanced view of a real-world environment, in which relevant content canbe overlaid onto physical objects or points of interest located withinthe environment. For example, a mobile AR application executable at auser's mobile device may overlay static or dynamic AR content related tovarious physical objects in the real-world environment. Each physicalobject may be identified by the mobile AR application as a predefined“target” that can be represented digitally within a virtual ARenvironment displayed for the user via a display of the mobile device.The virtual AR environment and objects represented may be based on, forexample, images of the real-world environment captured by a digitalcamera integrated with the mobile device. Examples of different types ofAR content that may be overlaid for an object in the real-world (e.g.,identified as a target in the virtual AR world) may include, but are notlimited to, text, images, graphics, or location information, e.g.,geographic location coordinates from a global positioning system (GPS).

However, conventional techniques for identifying targets within ARenvironments provided by mobile AR applications generally require acompromise between speed (e.g., with respect to AR target identificationand content delivery) and efficiency (e.g., with respect to memory usagefor storing target data). For example, speed may be increased by storingstatic target data for every identifiable target within a local memoryof the mobile device. However, this would place a significant burden onmemory and other system resources, particularly if the number ofidentifiable targets is extremely large (e.g., over a million or billiontargets).

Alternatively, efficiency may be improved by offloading targetidentification operations to a server and/or storing the target data ina remote storage location (e.g., a cloud-based data store), which may bedynamically accessed by the mobile AR application via a communicationnetwork. However, the speed of target identification at the mobiledevice may be significantly reduced due to, for example, datapropagation delays or bandwidth issues in the network.

Thus, such conventional solutions for target identification based oneither static local data or dynamic data located remotely posesignificant challenges to providing support for AR environmentsrepresenting relatively more complex real-world environments with largenumbers of identifiable targets (e.g., millions of targets related toindividual consumer product items within a commercial business), itbecomes even more critical for the mobile AR application to quicklyidentify a large number targets, while also maintaining an acceptable ordesired level of responsiveness and overall system performance.

SUMMARY OF THE DISCLOSURE

According to certain embodiments, methods are disclosed for predictingpotential targets to be identified in a mobile augmented realityapplication based on coincident targets identified previously, and forpredicting potential future targets for a user of a mobile augmentedreality application based on high-correlating targets identified duringa session of user activity.

According to certain embodiments, a method is disclosed fortransmitting, to user devices, data for potential targets predicted tobe identified in an augmented reality application. One method includesreceiving, over a network, a request from a user's device for targetdata related to at least one physical object within an image of areal-world environment captured at the device; responsive to thereceived request, identifying a current target representing the physicalobject within a virtual environment corresponding to the real-worldenvironment; identifying coincident target requests based on theidentified current target and a log of prior requests for targetsidentified previously; determining at least one potential future targetto be identified at the device based on the identified coincident targetrequests; and sending to the device, over the network, target data foreach of the current target and the potential future target based on thedetermination, wherein the device is configured to present the targetdata for the current target within the virtual environment displayed atthe device and store the target data for the potential future target ina local memory of the device.

According to certain embodiments, the target data for the current targetincludes predefined content to be displayed as an overlay correspondingto a location of the current target, as represented within the virtualenvironment.

According to certain embodiments, the determining step comprises:calculating correlation scores for respective targets related to thecoincident target requests, the correlation scores representing a degreeof correlation between each of the targets and the current target;assigning the calculated correlation scores to the respective targetsrelated to the coincident target requests; ranking the targets relatedto the coincident target requests based on the assigned correlationscores; and determining the at least one potential future target basedon the ranking.

According to certain embodiments, the at least one potential futuretarget corresponds to a predetermined number of high-ranking targetsdetermined to have a relatively high degree of correlation to thecoincident target requests.

According to certain embodiments, the identifying step comprises:identifying the coincident target requests based on the log of priorrequests and one or more correlation parameters associated with priorrequests in the log.

According to certain embodiments, the one or more correlation parametersinclude a time that each prior request for a previously identifiedtarget was sent by a device, a geographic location of a physical objectcorresponding to the previously identified target, and a user identifierassociated with a user of the device from which the prior request wassent.

According to certain embodiments, the one or more correlation parametersfurther include a predetermined target category associated with thepreviously identified target; the request is received during a currentsession of user activity and the log of prior requests corresponds totargets identified during one or more previous sessions of useractivity; the current session corresponds to a predetermined time periodof the user's interaction with a client application executable at thedevice; and/or the potential future target is to be identified prior toan expiration of the current session.

According to certain embodiments, a system is disclosed fortransmitting, to user devices, data for potential targets predicted tobe identified in an augmented reality application. One system includes adata storage device storing instructions for transmitting data forpotential targets predicted to be identified in an augmented realityapplication; and a processor configured to execute the instructions toexecute: a request manager to receive, over a network, a request from auser's device for target data related to at least one physical objectwithin an image of a real-world environment captured at the device; atarget identifier to identify a current target representing the physicalobject within a virtual environment corresponding to the real-worldenvironment, based on the request received by the request manager; atarget classifier to identify coincident target requests related to thereceived request for the current target based on a log of prior requestsfor targets identified during a session of user activity, and determineat least one potential future target to be identified at the devicebased on the identified targets related to the current target; and adata manager configured to send, to the device over the network, targetdata corresponding to each of the current target and the potentialfuture target, based on the determination by the target classifier,wherein the device is configured to provide a visual representation ofthe current target within the virtual environment based on thecorresponding target data, and store the target data for the potentialfuture target in a local memory of the device.

According to certain embodiments, the target classifier is configuredto: calculate a correlation score for each target related to thecoincident target requests, the correlation score representing a degreeof correlation between each of the targets and the current target;assign the calculated correlation scores to the respective targetsrelated to the coincident target requests; rank the targets related tothe coincident target requests based on the assigned correlation scores;and determine at least one potential future target based on the rankedtargets related to the coincident target requests.

According to certain embodiments, the at least one potential futuretarget corresponds to a predetermined number of high-ranking targetsdetermined to have a relatively high degree of correlation to thecoincident target requests; the target identifier is configured toidentify the coincident target requests based on prior requests in thelog of prior requests and one or more correlation parameters associatedwith prior requests in the log; the one or more correlation parametersinclude a time that a prior request for a previously identified targetwas sent by a device, a geographic location of a physical objectcorresponding to the previously identified target, and a user identifierassociated with a user of the device from which the prior request wassent; the one or more correlation parameters further include apredetermined target category associated with the previously identifiedtarget; the request for target data is received by the request managerfrom the user's device during a current session of user activity and thecoincident target requests related to the current target are based onprior requests received during one or more previous sessions of useractivity; the potential future target is to be identified prior to anexpiration of the current session; the current session corresponds to apredetermined time period of the user's interaction with a clientapplication executable at the device.

According to certain embodiments, a computer readable storage medium isdisclosed including instructions that, when executed by a computer,cause the computer to perform a plurality of functions, includingfunctions to: receiving a request from a user's device for target datarelated to at least one physical object within an image of a real-worldenvironment captured at the device; responsive to the received request,identifying a current target representing the physical object within avirtual environment corresponding to the real-world environment;identifying coincident target requests based on the identified currenttarget and a log of prior requests for targets identified previously;determining at least one potential future target to be identified at thedevice based on the identified coincident target requests; and sendingtarget data for each of the current target and the potential futuretarget via a network to the device based on the determination, whereinthe device is configured to present the target data for the currenttarget within the virtual environment displayed at the device and storethe target data for the potential future target in a local memory of thedevice.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 illustrates an exemplary real-world environment in which targetdata for potential targets to be identified by a mobile AR applicationexecutable at a user's mobile device may be transmitted to the mobiledevice,

FIG. 2 is a diagram of an exemplary network environment and systemssuitable for practicing an embodiment of the present disclosure.

FIG. 3 is a block diagram of an exemplary client-server system fortransmitted target data related to potential future targets that may beidentified for a user of a mobile AR application during a session ofuser activity.

FIG. 4 is a flowchart of an exemplary method for transmitted target datarelated to potential future targets that may be identified for a user ofa mobile AR application during a session of user activity.

FIG. 5 is a process flowchart of exemplary method for determiningpotential targets to be identified in the mobile AR application methoddescribed in FIG. 4.

FIG. 6A illustrates an exemplary array of targets and target dataassociated with augmented reality targets for a mobile AR application.

FIG. 6B illustrates an exemplary request log of user requests related tothe augmented reality targets of FIG. 6A.

FIG. 7 is a simplified block diagram of an exemplary computer system inwhich embodiments of the present disclosure may be implemented.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

The present disclosure relates to streaming or otherwise transmittingtarget data for targets (or “future targets”) predicted to bepotentially identified by a mobile AR application executable at a user'smobile device, based on coincident requests for related targetspreviously identified during a session of user activity. The word“target(s)” is used herein to describe a virtual representation oridentification of a real-world, i.e., physical, object. In oneembodiment, an AR application server may stream or otherwise transmitthe target data to a local AR cache within a memory of the mobiledevice. The local AR cache may be one or more predefined or dynamicallyallocated memory blocks of any size, which may be reserved for storingtarget data. The mobile AR application in this example may be configuredto identify targets corresponding to physical objects to be representedwithin an AR environment accessed based on the target data stored withinthe local AR cache at the mobile device.

For example, in some cases, a user may be in a particular real worldenvironment, and may be encountering one or more real world objects,such as a product, a printed article of content, an electronic displayof content, a location, etc. The user may scan or otherwise request ARdata associated with the particular object in the real worldenvironment. According to certain aspects of the present disclosure, theuser's electronic device may cause an AR server to begin predicting,based on the identity and other aspects of the scanned objects, whatother future objects the user might scan. Accordingly, the AR server maypreemptively stream or otherwise send to the user's device the AR dataassociated with the one or more future targets that the AR serverpredicts the user will likely soon encounter and scan to requestassociated AR data.

As will be described in further detail below, the virtual AR environmentand objects represented may correspond to a current view of the realworld based on images captured by a digital camera integrated with themobile device and displayed for the user via a display of the mobiledevice. A physical object in the real world may correspond to apredefined reference marker or “target” in the virtual AR world providedfor the user by the mobile AR application at the mobile device. ARcontent associated with a physical object (or predefined AR target) mayinclude, for example and without limitation, text, images, graphics, orlocation information, e.g., geographic location coordinates from aglobal positioning system (GPS), and/or textual, image, video, or audioadvertisements(s) or sponsored content.

FIG. 1 illustrates an exemplary real-world environment 100, in whichtarget data may be transmitted to a mobile device 104 of a user 102, forpotential future targets likely to be identified by a mobile ARapplication of the device, based on coincident requests for targetsidentified during a session of user activity. In the example shown inFIG. 1, user environment 100 may be a storefront of a commercialbusiness 101, which displays various products or objects. For example,user environment 100 may be a shopping mall, grocery, restaurant,department store, boutique store, transit location (e.g., airport, trainstation, bus stop, rest area, etc.), or the like. The user environment100 may alternatively be an amusement park, concert venue, museum, park,or any other tourist attraction having physical objects or locations ofinterest to one or more users. In one embodiment, the user environment100 may include a printed or electronic display of content, such as anelectronic screen or touchscreen displaying electronic content to theuser, or a newspaper, magazine, or sign displaying printed content tothe user, in which cases an object having associated AR target data maybe any printed or electronic content (e.g., an article, image, ad,etc.).

As shown in FIG. 1, in one embodiment, the user environment 100 mayinclude a first zone 110 having a plurality of objects 112, and a secondzone 120 having a plurality of objects 122, and each of the objects 112,122 may be identifiable by a mobile device 104. According to certainaspects of the present disclosure, a user 102 may operate a mobileapplication executed on mobile device 104 to request and obtain virtualor AR data associated with one or more of objects 112, 122. As describedabove, the present disclosure is directed to improving both the speedand efficiency at which the user's device 104 may request and obtainsuch virtual or AR data. For example, if a user 102 requests AR data foran object 122, an associated AR server may determine that the user 102is more likely to subsequently request AR data for another one ofobjects 122 within zone 120, and therefore preemptively stream dataassociated with each of objects 122 to the user's device 104. Likewise,if a user 102 requests AR data for an object 112, an associated ARserver may determine that the user 102 is more likely to subsequentlyrequest AR data for another one of objects 112 within zone 110, andtherefore preemptively stream data associated with each of objects 112to the user's device 104.

Although FIG. 1 depicts zones 110 and 120 as physical zones defined byspace, it should be appreciated that zones 110, 120 may alternatively besubject matter categories, product types, stores, brands, etc. Ofcourse, instead of preemptively streaming data associated with an entirezone 110, 120, the associated AR server might preemptively stream dataassociated with only a subset of the similar objects to the user (e.g.,for those objects for which a user is statistically more likely torequest AR data in the near future). Moreover, the associated AR servermay preemptively stream any desired amount of data associated with anydesired number of predicted future objects (i.e., AR targets). Forexample, the associated AR server may preemptively stream to a user'sdevice the AR data associated with the “N” next objects the user is mostlikely to scan or otherwise request, regardless of which “zone” suchobjects may exist within.

FIG. 2 is a schematic diagram of an environment in which devices andservers may interact to perform methods of transmitting and caching dataon mobile devices in relation to physical locations and objects likelyto be identified and targeted for display in mobile augmented realityapplications, according to an exemplary embodiment of the presentdisclosure. Specifically, FIG. 2 illustrates an exemplary environment200 including one or more user devices 210, and/or one or more serversystems 220, 230, 240. The present disclosure is applicable to datatransfer between any combination of user devices 210, and server systems220, 230, 240.

As depicted in FIG. 2, user devices 210 may include, for example, amobile phone 202, a mobile device 204, a tablet 206, and/or a laptop,computer or server 208. In one embodiment, user devices 210 may be ownedand used by one or more people, who may be viewers of web pages and/orconsumers of content over the Internet, either through an application,mobile browser, or web browser stored on respective user devices. Userdevices 210 may include any type of electronic device configured to sendand receive data, such as websites, multimedia content, and electronicadvertisements, over electronic network 201. For example, each of userdevices 210 may include a mobile device, smartphone, personal digitalassistant (“PDA”), a navigation device, a portable game console,personal computer, laptop, server, or any combination of these computingdevices or other types of mobile computing devices having at least oneprocessor, a local memory, a display, one or more user input devices,and a network communication interface disposed in communication withelectronic network 201.

Each of user devices 210 may have a web browser and/or mobile browserinstalled for receiving and displaying electronic content received fromone or more web servers. Each of user devices 210 may have an operatingsystem configured to execute a web or mobile browser, and any type ofapplication, such as a mobile application. In one embodiment, userdevices 210 may be configured to download applications and/orapplication content from application servers. In one embodiment, userdevices 210 may be configured to download one or more augmented realitymobile applications from a mobile application server, and execute thoseone or more augmented reality applications to receive and manipulateelectronic augmented reality content received from one or more of webservers and/or content distribution servers.

As shown in FIG. 2, the environment 200 may include one or moreaugmented reality and/or advertising servers 220, one or more vendorand/or third party augmented reality servers 230, and one or moreauxiliary augmented reality servers 240. The one or more server systems220, 230, 240 may include any type of web server, service, or any othertype of web resource that may be accessed by one or more of user devices210, and/or each other. Specifically, the one or more server systems220, 230, 240 may represent any of various types of servers including,but not limited to a web server, an application server, a proxy server,a network server, or a server farm. The one or more server systems 220,230, 240 may be implemented using any general-purpose computer capableof serving data to user devices 210 or any other computing devices (notshown) via network 201. Such other computing devices may include, butare not limited to, any device having a processor and memory forexecuting and storing instructions. Software may include one or moreapplications and an operating system. Hardware may include, but is notlimited to, a processor, e.g., processors 224, 234, a memory device,e.g., databases 226, 236, and modules 222, 232 of graphical userinterface(s) (“GUIs”), and/or application programming interface(s)(“APIs”). The computing device may also have multiple processors andmultiple shared or separate memory components. For example, thecomputing device may be a clustered computing environment or serverfarm.

While only servers 220, 230, 240 are shown, additional servers may beused as desired. Similarly, while only user devices 210 are shown,network environment 200 may include, for any number of users, additionaluser devices that also may be configured to communicate with servers220, 230, 240 via network 201. Network 201 can be any network orcombination of networks that can carry data communication. Network 201may include, but is not limited to, a local area network, medium areanetwork, and/or wide area network, such as the Internet. Any or all ofthe devices 210 and/or servers 220, 230, 240 may be disposed incommunication with an electronic network 201, such as the Internet.Moreover, any pair of the devices 210 and/or servers 220, 230, 240 maybe configured to exchange data packets over the electronic network 201according to any suitable predetermined protocol, such as hypertexttransfer protocol (“HTTP”). Any or all of the devices 210 and/or servers220, 230, 240 may be configured to perform various techniques forexchanging requests for data, responses to requests for data, and data,in manners so as to transmit and cache data on mobile devices inrelation to physical locations and objects likely to be identified andtargeted for display in mobile augmented reality applications.

As shown in FIG. 2, server systems 220, 230 may each include or beprovided in communication with one or more databases 226, 236. Databases226, 236 may be any type of database or data repository for storing dataaccessible by server systems 220, 230. Although only databases 226, 236are shown, additional databases may be used as desired. As will bedescribed in further detail below, databases 226, 236 may be used tostore, for example, data related to various targets representingphysical real-world objects in a mobile AR application (e.g., a clientapplication) executable at each of user devices 210. Also, as will bedescribed in further detail below, server systems 220, 230 in thisexample may be an application server configured to serve target data forpotential targets to be identified for a user of client application ateach of user devices 210. In a further example, a client application mayprovide an interface at each of user devices 210 for acquiring data orexecutable software instructions from an associated AR application orservice hosted at servers 220, 230 via network 201.

FIG. 3 is a block diagram of an exemplary client-server arrangement 300that may be implemented by user devices 210 and server system(s) 220 ofFIG. 2, for streaming or otherwise transmitting target data forpotential targets predicted to be identified in a mobile AR application.Specifically, client-server arrangement 300 may be a client-serverimplementation of the network environment 200 that spans one or morenetworks, such as a network 201 of FIG. 2. As shown in FIG. 3,client-server arrangement 300 may include a user device 210 that iscommunicatively coupled to a server 220 via electronic network 201.Server 220 in turn may be coupled to a database 335, which may bedatabase 226. User device 210 may be implemented using, for example, anyof user devices 210 of FIG. 2, as described above. Similarly, server 220and database 335 may be implemented using, for example, any of theserver systems 220, 230, 240 of FIG. 2, also as described above.

As shown in the embodiment of FIG. 3, user device 210 may include aclient application 315 and a memory 318. Client application 315 in thisexample may be mobile AR application executable at user device 310.Client application 315 may be implemented as, for example, a standaloneapplication, or it can be executed within a web browser at user device210 as, for example, a browser plug-in or script that executes withinthe browser. As shown in FIG. 3, client application 315 may beconfigured to communicate via network 201 with an associated ARapplication or service hosted at server 220 in order to provide ARfunctionality to a user of client application 315 at user device 210.

In the example shown in FIG. 3, client application 315 may include agraphical user interface (GUI) 316 and a target identifier 317. GUI 316may be adapted to provide a virtual representation of a real-worldenvironment including AR content related to one or more physical objectsor points of interest located within the real-world environment via adisplay (e.g., a touch-screen display) of user device 210. As describedabove, such a virtual AR environment may be based on, for example, animage or video captured by a digital camera (not shown) that may becoupled to or integrated with user device 210. The captured image(s) maybe of a single object or a real-world scene including multiple objects.In addition, the captured image may be, for example and withoutlimitation, a barcode, such as a Universal Product Code (UPC), QR-Code,or other type of machine-readable or coded symbol that uniquelyidentifies a physical object and is generally imprinted on the objectitself or a label attached to the object. In some implementations, userdevice 210 may include a barcode scanner for scanning such a codedsymbol.

Target identifier 317 in this example may be adapted to identify targetscorresponding to the physical object(s) or point(s) of interest within acaptured image of the real-world environment. The targets identified bytarget identifier 317 may be based on, for example, target data storedin an AR cache 319 of local memory 318. As described above, the targetdata stored in AR cache 319 may have been obtained from server 220 vianetwork 201. In an example, target identifier 317 may detect an objectwithin the real-world environment and attempt to find matching targetdata stored within AR cache 319.

In some implementations, target data stored within AR cache 319 mayinclude, for example, information associated with a correspondingphysical object within the real-world environment. Such information mayinclude, for example, a definition of characteristic features orphysical parameters associated with the object in the real-world, whichmay be used by target identifier 317 to recognize the object detected inthe real-world environment as a target within an AR environment. Thisinformation may also be used to generate a visual representation of thephysical object (or target) within the virtual AR environment. Asdescribed above, the AR environment and objects represented within theAR environment may be displayed by, for example, GUI 316 via a displayof user device 210. According to certain aspects of the presentdisclosure, if target identifier 317 in the above example does not findtarget data within AR cache 319 that matches the detected object, targetidentifier 317 may send a request for the matching target data to server220 via network 210.

As shown in FIG. 3, in one embodiment, the server 220 may include, amongother modules and devices, a content manager 332, a target identifier334, and/or a target classifier 336. Of course, server 220 may includeother modules and devices, and server 220 may share the functions ofcontent manager 332, target identifier 334, and/or target classifier 336across other servers, such as server systems 230, 240 of FIG. 2. Thecontent manager 332, target identifier 334, and/or target classifier 336may be software modules executed by processor 224 of servers 220, or mayalternatively be separate or integrated processing devices themselves.The content manager 332 may be configured to receive and aggregate ARcontent (e.g., ads, text, images, etc.) from any of the database(s) 335,database(s) 226, the vendor/third party AR target server(s) 230, andauxiliary server(s) 240. In one embodiment, server(s) 220 may alsoinclude a request manager configured to receive, over network 210, oneor more requests from a user's mobile device 210 for target data relatedto one or more physical objects within an image of a real-worldenvironment captured at the mobile device.

In one embodiment, the target identifier 334 may be configured toidentify a current target representing the physical object within avirtual environment corresponding to the real-world environment, basedon the one or more requests received at the server(s) 220, e.g., by therequest manager. The target classifier 336 may be configured to identifycoincident target requests related to a request received in relation toeach current target, such as based on a log of prior requests fortargets identified during a session of user activity, and determine atleast one potential future target to be identified at the mobile devicebased on the identified targets related to the current target. Server(s)220 may also include a data manager configured to send, to user device210 over network 201, target data corresponding to each of the currenttarget and the potential future target, based on the determination bythe target classifier. The user device 210 may be configured to providea visual representation of the current target within the virtualenvironment based on the corresponding target data, and store the targetdata for the potential future target in a local memory of the device.

FIG. 4 is a flowchart of an exemplary method 400 for streaming orotherwise transmitting target data for potential targets predicted to beidentified by a mobile AR application. As shown in FIG. 4, method 400may be performed between any user device and a server, e.g., anaugmented reality (AR) server. Specifically, the method 400 of FIG. 4may be performed by a user device 210 and a server system 220, describedwith respect to FIGS. 2 and 3. Alternatively or additionally, any of theAR server steps of method 400 may be performed by one or more of serversystems 230, 240, of FIG. 2.

As shown in the embodiment of FIG. 4, method 400 may include a userdevice scanning or photographing an image or barcode of a target (step402). For example, a user 102 may use a mobile device 104, 210 torequest target data for a physical object or location. For example, auser 102 may approach one or more physical objects 112, 122 and desireto view additional virtual data (e.g., photo(s), textual description(s),pricing information, related URL(s), audio recording(s), discountcodes/coupons, nutritional info, recipes, etc.) regarding those objects.In one embodiment, the user 102 may request data relating to the objectby obtaining a photograph of the object using device 104, 210.Alternatively, the user 102 may execute an application of the device104, 210 to perform image and/or textual recognition of the object,and/or to read or decode a one or two dimensional barcode or otheridentifying indicia on the object.

Method 400 may then include checking for data related to the identifiedtarget on the user's device, e.g., in cache or other local memory (step404). For example, upon the user's device recognizing the object and/oridentifying a target ID associated with the object or location inproximity of the user's device, the device may look-up the target ID inthe cache or local memory to obtain any stored AR target data associatedwith the target ID. At any time before, after, or during transmission ofany image, barcode, or other identifying information from the userdevice to the AR server, the user's device may display any non-dynamictarget content that is retrieved in relation to the image or barcodedata (step 410). That is, if at step 404, the user device finds relatedAR target data while performing a local check for that data, the devicemay display that AR target data to the user. For example, the userdevice may display an electronic reproduction of the physical object,e.g., as through a viewfinder of the user's device, and overlaid on thatelectronic reproduction, also display one or more virtual data items(e.g., photo(s), textual description(s), pricing information, relatedURL(s), advertisements, etc.).

As described above, at any time before, during, or after checking forand/or displaying locally-stored related AR data, method 400 may includesending an image, barcode data, or other identifying information aboutthe object, or the target of the object, to the AR server (step 406).Thus, the AR server, e.g., server systems 220, may receive the image,barcode, or other identifying information from the user device (step408). Regardless of whether the user's device found locally-stored,non-dynamic target AR data to display to the user, upon receiving theimage, barcode, or other identifying information from the user device,the AR server may look-up and send AR data related to the identifiedtarget back to the user device (step 412). For example, as shown in FIG.6A, the augmented reality and/or advertising server systems 220 maymaintain a database 226 of AR targets, AR target IDs, and related targetdata for any number of physical objects. Of course, it should beappreciated that, although FIG. 6A only depicts six exemplary targetswith associated target IDs and related data, in operation, the databases226 may store data associated with millions, tens of millions, orbillions of different objects, such as different products, digitalitems, people, consumer devices, tourist attractions, content items,etc. Thus, as shown in FIG. 2, augmented reality and/or advertisingserver systems 220 may be provided in communication with one or both ofthe vendor/third party AR target server(s) 230 and the auxiliary ARserver(s) 240. Thus, while looking up AR target IDs and related targetdata for any given AR target or object, the augmented reality and/oradvertising server systems 220 may additionally issue requests to thevendor/third party AR target server(s) 230 and/or the auxiliary ARserver(s) 240 for AR target IDs and related target data. As shown inFIG. 6A, any of those databases may store, for any given target of aphysical object or location, a target ID (e.g., A_(ID), B_(ID), etc.),and perhaps some textual data (e.g., A_(TEXT)), a photo, graphic,recording, etc. (e.g., A_(GRAPHIC)), and/or a URL or other Internetobject (e.g., A_(LINK)).

Upon retrieving related AR data related to the target, the AR server maysend the related information back to the user's device, e.g., overelectronic network 201 and any related cellular, Wi-Fi, or othernetworks, such that the user's device 104, 210 may receive and store therelated information on local memory for presentation to the user (step414). Of course, the user's device 104, 210 may present the related ARtarget data received from the server to the user using any known means,such as by presenting textual and/or visual/imagery data to the user ona display or touchscreen, displaying data overlaid on top of a digitalrepresentation of the physical object or location, displaying the dataon a map of the location or target, and/or playing an audio sound, etc.

As the user continues to encounter various physical objects orlocations, as desired, the user may continue to selectively scan,decode, photograph, or otherwise request AR data associated with thosetargets, and therefore continue to repeat steps 402-414. That is, theuser's device may continue to check the local cache or memory forrelated data (step 404), and if necessary, request such data from the ARserver (step 406). In some cases, the user's device may have storedtherein some non-dynamic AR data related to the identified object,whereas in others, the AR server may send the user's device dynamic ornon-dynamic AR data related to the identified object.

According to certain aspects of the present disclosure, when the ARserver looks-up and sends to the user device some information related tothe identified object or location, the AR server may perform additionalsteps for performing predictions of objects and locations likely to berequested by the user in the near future. For example, in oneembodiment, the AR server may perform the steps of logging co-incidentrequests from the user device (step 416), identifying one or morehigh-scoring targets correlating to the co-incident requests (step 418),and streaming or otherwise sending to the user device informationrelated to the one or more high scoring targets (step 420). In otherwords, the AR server may determine what one or more targets the user islikely to scan next, and preemptively stream AR data relating to thosepredicted targets to the user's device before the user has scanned suchobjects.

Specifically, in one embodiment, each time AR server receives an AR datarequest (step 408) and looks-up and sends related AR data to user'sdevice (step 412), the AR server may also store in a database, e.g.,databases 226, 335, information related to the request, including, e.g.,the identity of the user making the request, the time of the request,the contents of the request (e.g., photo, barcode, etc.), and/or thedata that was retrieved and sent back to the user's device in responseto the request (step 416). Accordingly, the AR server may generate a logof user requests, which may be indexed and searchable by user, targetID, timestamps, categories, etc.

The AR server may then, at any time during method 400, identify highscoring targets correlating to the co-incident requests (step 418). FIG.5 is a process flowchart of an exemplary method 500 for predictingpotential targets to be streamed or otherwise transmitted to the mobileAR application of FIG. 3. As shown in FIG. 5, method 500 may includefiltering the target request log (e.g., generated at step 416) forcoincident target requests related to the current or last-requestedtarget (step 502). Method 500 may further include calculating one ormore probabilities that various potential future targets will berequested, based on an evaluation of the coincident target requests(step 504), using any suitable formula or algorithm, such as a machinelearning algorithm and/or an artificial intelligence algorithm. Forexample, in one embodiment, the AR server may determine the “N” objectsthe user is most likely to scan or otherwise request next. In oneembodiment, the AR server may determine, based on historicalinformation, such as the created log of co-incident requests, thestatistical likelihood that the user will request AR data for anyparticular AR target. In a database of millions of targets, for 99% ofthe targets, the likelihood of a user's subsequent selection of thatrespective target within some preset time period might be less than0.01%. However, for some small subset of targets, there might be a >10%or maybe even >50% chance that the user will request AR data for thosetargets. As described above with respect to the simplified example ofFIG. 1, if a user has already requested AR data for three of the fourobjects 122 in zone 120, the AR server might determine that there isa >50% chance that the user will next request AR data for the fourth andfinal object 122 in zone 120.

FIG. 6B illustrates an exemplary request log of user requests related tothe predefined targets of FIG. 6A, as an exemplary use case forstreaming target data for potential targets to be identified in a mobileAR application. As an overly simplified example, if users 1 and 2requested AR data for AR targets “B” and then “C,” in that order, anduser 3 requested AR data for AR target “B” and then “D,” then the ARserver may determine that a subsequent user might have a 66% probabilityof subsequently requesting AR data for AR target “C,” after requestingAR data for target “B.” Thus, consistent with the presently disclosedembodiment, the AR server may determine to preemptively send AR dataassociated with AR target “C” (along with AR data associated with ARtarget “B”) to a user that has just requested AR data for target “B.”Likewise, if each of users 1-3 has requested AR data for target “E”within at least, e.g., five, AR requests from requesting AR data forboth of targets “B” and “D,” then the AR server might conclude thatthere is a high degree of co-incidence or correlation between thosetargets, and therefore preemptively transmit AR data for target “E” to auser device of another user that has only requested AR data for targets“B” and “D.” Of course, the AR server may take into account thebehavioral history of any number of other users, and may determine therequest likelihood for any number of potentially requested AR targets.

Method 500 may further include ranking the targets from coincidentrequests according to the calculated probabilities (step 506). Forexample, the AR server might identify, for example, the N number oftargets (e.g., 10, 20, 50, 100, etc.) for which there is at least some X% threshold likelihood (e.g., 20%, 50%, 70%) that the user will requestAR data for that target within a threshold amount of time, T, (e.g., 10min, 1 hour, 24 hours, etc.). In one embodiment, the qualifying targetsmay be ranked in order and/or scored for likelihood of subsequentselection. Method 500 may further include retrieving target dataassociated with high ranking coincident requests (step 508), for sendingback to the user device. For example, in one embodiment, the contentmanager 332 of server(s) 220 may retrieve AR target data of theidentified AR targets from one or more of database(s) 335, database(s)226, the vendor/third party AR target server(s) 230, and auxiliaryserver(s) 240.

In one embodiment, a portion of the local memory at the mobile devicemay be dedicated as a target data cache for use in target identificationby the client application based on real-world objects detected at themobile device during the current session. In one embodiment, theidentification of targets and high ranking coincident targets may bebased on previously identified targets of prior requests received duringone or more previous sessions of user activity. For example, theprevious sessions may correlate to a current geographic location of themobile device during the current session. The current and potentialfuture targets may be associated with a target category according to oneor more predetermined correlation parameters. The predeterminedcorrelation parameters may include location, time, target type, user ID(e.g., where a higher score is generated for past target requests by thesame user relative to past requests from other users, to account forindividual patterns or behavioral characteristics of different users).The analysis of coincident requests may include identifying coincidenttarget requests received (from same or different users) within apredetermined period of time during the current session. The currentsession may be any predetermined period of activity associated with oneor multiple users of the AR application/content delivery service.

For example, in one embodiment, a “current session” may be a singlesession of user activity by the same user (e.g., where a single sessionfor a user may correspond to a period of time starting from when theuser launches the app at the device, then performs some activity suchthat a target request is received by the server (and the server logs therequest) and ending when the user closes the app or discontinues use fora certain period of time (e.g., no indication of user activity isreceived at the server for a predetermined period of time). In oneembodiment, the server may be configured to log only those user requestsreceived during the current session. As such, the number of requestslogged for the user or user's device may vary depending on how a currentsession is defined. For example, the current session may be any numberof usage sessions over a predetermined period of time by the same user;one or more distinct predetermined time period of usage or user activityby the same user (e.g., limited to each usage of application, where onlytarget requests occurring during a single usage session are logged.Alternatively, a user session may include any target request receivedfrom the same user's device within the same hour, day, week, etc. In yetanother embodiment, a user session may take into consideration the ARrequests for any user across any device, over any past amount of time.

In one embodiment, coincident requests or AR targets may be determinedbased on event logs stored at the device (and accessible by server orreceived in the target request) or stored at the server for eachuser/mobile device (server may assign a unique user/device ID for eachregistered user/device and maintain a target request history or sessionlog for the user/device based on user activity/interaction with the ARapp at the device.

In one embodiment, the AR server may perform a statistical analysis ofusage data across multiple users for relevant time period to determineusage/target-scan patterns, which may be used to determine/predicttargets that are likely to be scanned in the future. Such a statisticalanalysis may be individualized for the current user based on preferencesspecified by the user through the AR app or account info registered forthe user or device at the server. In another embodiment, thedetermination of previous targets correlating to a current target may beperformed based on the generated request log, with coincident requestsbeing from same user or different users for target requests receivedduring a “current session” of user activity (depending on the definitionof current session). In one embodiment, the AR application may beconfigured to send automatic notifications, e.g., as a backgroundprocess, to the server based on user interaction with a GUI of the ARapp.

Method 400 may further including streaming or otherwise sending, fromthe AR server to the user device, any information relating to theidentified high-scoring targets (step 420). In one embodiment, theidentified AR data for the identified or predicted AR targets may beretrieved from another location, such as from vendor/third party ARtarget server(s) 230 and/or auxiliary AR server(s) 240. In any event,one or more AR servers may transmit the identified and retrieved AR datafor the predicted AR targets to the user device 210 over electronicnetwork 210, including over any related cellular and/or Wi-Fi network.Thus, the user's device may receive and store in cache or local memorythe information related to the one or more high scoring targets (step422). Because the user may or may not request AR data for the targetsfor which the user device preemptively received related AR data, it maybe advantageous for the user device to execute an expiration policy todelete undesired or unused information or AR data from the user's devicecache or memory (step 424).

FIG. 7 is a simplified block diagram of an exemplary computer system 700in which embodiments of the present disclosure may be implemented. Aplatform for a server or the like 700, for example, may include a datacommunication interface for packet data communication 760. The platformmay also include a central processing unit (CPU) 720, in the form of oneor more processors, for executing program instructions. The platformtypically includes an internal communication bus 710, program storageand data storage for various data files to be processed and/orcommunicated by the platform such as ROM 730 and RAM 740, although theserver 700 often receives programming and data via a communicationsnetwork (not shown). The hardware elements, operating systems andprogramming languages of such equipment are conventional in nature, andit is presumed that those skilled in the art are adequately familiartherewith. The server 700 also may include input and output ports 750 toconnect with input and output devices such as keyboards, mice,touchscreens, monitors, displays, etc. Of course, the various serverfunctions may be implemented in a distributed fashion on a number ofsimilar platforms, to distribute the processing load. Alternatively, theservers may be implemented by appropriate programming of one computerhardware platform.

As described above, the computer system 700 may include any type orcombination of computing systems, such as handheld devices, personalcomputers, servers, clustered computing machines, and/or cloud computingsystems. In one embodiment, the computer system 700 may be an assemblyof hardware, including a memory, a central processing unit (“CPU”),and/or optionally a user interface. The memory may include any type ofRAM or ROM embodied in a physical storage medium, such as magneticstorage including floppy disk, hard disk, or magnetic tape;semiconductor storage such as solid state disk (SSD) or flash memory;optical disc storage; or magneto-optical disc storage. The CPU mayinclude one or more processors for processing data according toinstructions stored in the memory. The functions of the processor may beprovided by a single dedicated processor or by a plurality ofprocessors. Moreover, the processor may include, without limitation,digital signal processor (DSP) hardware, or any other hardware capableof executing software. The user interface may include any type orcombination of input/output devices, such as a display monitor,touchpad, touchscreen, microphone, camera, keyboard, and/or mouse.

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms, such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

While the presently disclosed sharing application, methods, devices, andsystems are described with exemplary reference to mobile applicationsand to transmitting HTTP data, it should be appreciated that thepresently disclosed embodiments may be applicable to any environment,such as a desktop or laptop computer, an automobile entertainmentsystem, a home entertainment system, etc. Also, the presently disclosedembodiments may be applicable to any type of Internet protocol that isequivalent or successor to HTTP.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A method for transmitting, to user devices, datafor potential targets predicted to be identified in an augmented realityapplication, the method comprising: receiving, over a network, a requestfrom a device of a user for target data related to at least one physicalobject within an image of a real-world environment captured at thedevice during a current session; responsive to the received request,identifying a current target representing the physical object within avirtual environment corresponding to the real-world environment;identifying coincident target requests for the device based on theidentified current target for the device and a log of prior requests fortargets identified previously for the device during one or more previoussessions; determining at least two potential future targets to beidentified at the device based on the identified coincident targetrequests for the device and on identified coincident target requestsfrom one or more other devices; calculating, for each potential futuretarget, a correlation score based on the coincident target requests, thecorrelation scores representing a degree of correlation between eachpotential future target and the current target, the degree ofcorrelation relating to a likelihood of a future selection by the user,wherein the degree of correlation is based on behavioral history of theuser and a plurality of other users, and whether the potential futuretarget is selected by the user within a predetermined amount of time;assigning each calculated correlation score to the respective potentialfuture target; ranking the at least two potential future targets basedon the assigned correlation scores; determining at least one potentialfuture target to send to the device based on the ranking of the at leasttwo potential future targets; and sending to the device, over thenetwork, target data for each of the current target and the determinedat least one potential future target, wherein the device is configuredto present the target data for the current target within the virtualenvironment displayed at the device and store the target data for thedetermined at least one potential future target in a local memory of thedevice.
 2. The method of claim 1, wherein the target data for thecurrent target includes predefined content to be displayed as an overlaycorresponding to a location of the current target, as represented withinthe virtual environment.
 3. The method of claim 2, wherein the at leasttwo potential future targets correspond to a predetermined number ofhigh-ranking targets determined to have a degree of correlation to thecoincident target requests greater than a predetermined threshold. 4.The method of claim 1, wherein the identifying step comprises:identifying the coincident target requests based on the log of priorrequests and one or more correlation parameters associated with priorrequests in the log.
 5. The method of claim 4, wherein the one or morecorrelation parameters include a time that each prior request for apreviously identified target was sent by a device, a geographic locationof a physical object corresponding to the previously identified target,and a user identifier associated with a user of the device from whichthe prior request was sent.
 6. The method of claim 4, wherein the one ormore correlation parameters further include a predetermined targetcategory associated with the previously identified target.
 7. The methodof claim 1, wherein the request is received during the current sessionof user activity and the log of prior requests corresponds to targetsidentified during the one or more previous sessions of user activity. 8.The method of claim 7, wherein the current session corresponds to apredetermined time period of the user's interaction with a clientapplication executable at the device.
 9. The method of claim 7, whereinthe at least two potential future targets are identified prior to anexpiration of the current session.
 10. A system for transmitting, touser devices, data for potential targets predicted to be identified inan augmented reality application, the system comprising: a data storagedevice storing instructions for transmitting data for potential targetspredicted to be identified in an augmented reality application; and aprocessor configured to execute the instructions to execute: a requestmanager to receive, over a network, a request from a device of a userfor target data related to at least one physical object within an imageof a real-world environment captured at the device during a currentsession; a target identifier to identify a current target representingthe physical object within a virtual environment corresponding to thereal-world environment, based on the request received by the requestmanager; a target classifier to identify coincident target requestsrelated to the received request from the device for the current targetbased on a log of prior requests for targets identified during one ormore previous sessions of user activity of the device, and determine atleast two potential future targets to be identified at the device basedon the identified targets related to the current target and onidentified targets from one or more other devices, calculate, for eachpotential future target, a correlation score based on the coincidenttarget requests, the correlation scores representing a degree ofcorrelation between each potential future target and the current target,the degree of correlation relating to a likelihood of a future selectionby the user, wherein the degree of correlation is based on behavioralhistory of the user and a plurality of other users, and whether thepotential future target is selected by the user within a predeterminedamount of time, assign each calculated correlation score to therespective potential future target, rank the at least two potentialfuture targets based on the assigned correlation scores, and determineat least one potential future target to send to the device based on theranking of the at least two potential future targets; and a data managerconfigured to send, to the device, over the network, target datacorresponding to each of the current target and the determined at leastone potential future target, wherein the device is configured to providea visual representation of the current target within the virtualenvironment based on the corresponding target data, and store the targetdata for the determined at least one potential future target in a localmemory of the device.
 11. The system of claim 10, wherein the at leasttwo potential future targets correspond to a predetermined number ofhigh-ranking targets determined to have a degree of correlation to thecoincident target requests greater than a predetermined threshold. 12.The system of claim 10, wherein the target identifier is configured toidentify the coincident target requests based on prior requests in thelog of prior requests and one or more correlation parameters associatedwith prior requests in the log.
 13. The system of claim 12, wherein theone or more correlation parameters include a time that a prior requestfor a previously identified target was sent by a device, a geographiclocation of a physical object corresponding to the previously identifiedtarget, and a user identifier associated with a user of the device fromwhich the prior request was sent.
 14. The system of claim 13, whereinthe one or more correlation parameters further include a predeterminedtarget category associated with the previously identified target. 15.The system of claim 10, wherein the request for target data is receivedby the request manager from the device of the user during the currentsession of user activity and the coincident target requests related tothe current target are based on prior requests received during the oneor more previous sessions of user activity.
 16. The system of claim 15,wherein the at least two potential future targets are identified priorto an expiration of the current session.
 17. The system of claim 15,wherein the current session corresponds to a predetermined time periodof the user's interaction with a client application executable at thedevice.
 18. A non-transitory computer-readable storage medium includinginstructions that, when executed by a computer, cause the computer toperform a plurality of functions, including functions to: receiving arequest from a device of a user for target data related to at least onephysical object within an image of a real-world environment captured atthe device during a current session; responsive to the received request,identifying a current target representing the physical object within avirtual environment corresponding to the real-world environment;identifying coincident target requests for the device based on theidentified current target for the device and a log of prior requests fortargets identified previously for the device during one or more previoussessions; determining at least two potential future targets to beidentified at the device based on the identified coincident targetrequests for the device and on identified coincident target requestsfrom one or more other devices; calculating, for each potential futuretarget, a correlation score based on the coincident target requests, thecorrelation scores representing a degree of correlation between eachpotential future target and the current target, the degree ofcorrelation relating to a likelihood of a future selection by the user,wherein the degree of correlation is based on behavioral history of theuser and a plurality of other users, and whether the potential futuretarget is selected by the user within a predetermined amount of time;assigning each calculated correlation score to the respective potentialfuture target; ranking the at least two potential future targets basedon the assigned correlation scores; determining at least one potentialfuture target to send to the device based on the ranking of the at leasttwo potential future targets; and sending to the device, over thenetwork, target data for each of the current target and the determinedat least one potential future target, wherein the device is configuredto present the target data for the current target within the virtualenvironment displayed at the device and store the target data for thedetermined at least one potential future target in a local memory of thedevice.